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Model Approximation using magnitude and phase criteria: Implications for Model Reduction and System Identification

Date

2007

Authors

Sandberg, Henrik
Lanzon, Alexander
Anderson, Brian

Journal Title

Journal ISSN

Volume Title

Publisher

John Wiley & Sons Inc

Abstract

In this paper, we use convex optimization for model reduction and identification of transfer functions. Two different approximation criteria are studied. When the first criterion is used, magnitude functions are matched, and when the second criterion is used, phase functions are matched. The weighted error bounds have direct interpretation in a Bode diagram, and are suitable to engineers working with frequency-domain data. We also show that transfer functions that have similar magnitude or phase functions have a small relative H-infinity error, under certain stability and minimum phase assumptions. The error bounds come from bounds associated with the Hilbert transform operator restricted in its application to rational transfer functions. Furthermore, it is shown how the approximation procedures can be implemented with linear matrix inequalities, and four examples are included to illustrate the results.

Description

Keywords

Keywords: Frequency domain analysis; Hilbert spaces; Identification (control systems); Mathematical models; Optimization; Transfer functions; Hilbert transform; Model approximation; Model reduction; Semidefinite programs; Approximation theory Model approximation; Model reduction; Semidefinite programs; System identification

Citation

Source

International Journal of Robust and Nonlinear Control

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1002/rnc.1124

Restricted until

2037-12-31